{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec8fe53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "507cea74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#datapath = '../Ram/Data_Sets/'\n",
    "datapath = '../Ram/Data_Sets/'\n",
    "filename = 'boston.csv'\n",
    "trainfile = datapath+filename\n",
    "sep = '\\t'\n",
    "dft = pd.read_csv(trainfile, sep=sep)\n",
    "#dft.drop(['Churn2'],axis=1, inplace=True)\n",
    "print(dft.shape)\n",
    "target = ['MEDV']\n",
    "dft.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6bd0f6d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft[target[0]] = dft[target[0]].fillna(0)\n",
    "dft[target].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0dba194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = [x for x in list(dft) if x not in target]\n",
    "len(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a1957b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404, 13) (102, 13)\n"
     ]
    }
   ],
   "source": [
    "##get train and test data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(dft[preds], dft[target], test_size=0.20, random_state=1,\n",
    "#                                                    stratify=dft[target]\n",
    "                                                   )\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24adc52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(dft, test_size=0.20, random_state=1,\n",
    "#                                                    stratify=dft[target]\n",
    "                                                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4f5b1d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported 0.1.997 version. Select nrows to a small number when running on huge datasets.\n",
      "output = featurewiz(dataname, target, corr_limit=0.90, verbose=2, sep=',', \n",
      "\t\theader=0, test_data='',feature_engg='', category_encoders='',\n",
      "\t\tdask_xgboost_flag=False, nrows=None)\n",
      "Create new features via 'feature_engg' flag : ['interactions','groupby','target']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import featurewiz as FW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ceccce96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############################################################################################\n",
      "############       F A S T   F E A T U R E  E N G G    A N D    S E L E C T I O N ! ########\n",
      "# Be judicious with featurewiz. Don't use it to create too many un-interpretable features! #\n",
      "############################################################################################\n",
      "correlation limit = 0.9\n",
      "Skipping feature engineering since no feature_engg input...\n",
      "Skipping category encoding since no category encoders specified in input...\n",
      "#### Single_Label Regression problem ####\n",
      "    Loaded train data. Shape = (404, 14)\n",
      "#### Single_Label Regression problem ####\n",
      "loading the entire test dataframe - there is no nrows limit applicable #########\n",
      "    Loaded test data. Shape = (102, 14)\n",
      "#######################################################################################\n",
      "######################## C L A S S I F Y I N G  V A R I A B L E S  ####################\n",
      "#######################################################################################\n",
      "        No variables were removed since no ID or low-information variables found in data set\n",
      "No GPU active on this device\n",
      "    Tuning XGBoost using CPU hyper-parameters. This will take time...\n",
      "    After removing redundant variables from further processing, features left = 13\n",
      "No interactions created for categorical vars since feature engg does not specify it\n",
      "#######################################################################################\n",
      "#####  Searching for Uncorrelated List Of Variables (SULOV) in 13 features ############\n",
      "#######################################################################################\n",
      "    there are no null values in dataset...\n",
      "    Removing (1) highly correlated variables:\n",
      "    ['RAD']\n",
      "    Following (12) vars selected: ['AGE', 'B', 'CHAS', 'CRIM', 'DIS', 'INDUS', 'LSTAT', 'NOX', 'PTRATIO', 'RM', 'ZN', 'TAX']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken for SULOV method = 1 seconds\n",
      "    Adding 0 categorical variables to reduced numeric variables  of 12\n",
      "Final list of selected 12 vars after SULOV = ['AGE', 'B', 'CHAS', 'CRIM', 'DIS', 'INDUS', 'LSTAT', 'NOX', 'PTRATIO', 'RM', 'ZN', 'TAX']\n",
      "Converting all features to numeric before sending to XGBoost...\n",
      "#######################################################################################\n",
      "#####    R E C U R S I V E   X G B O O S T : F E A T U R E   S E L E C T I O N  #######\n",
      "#######################################################################################\n",
      "    using regular XGBoost\n",
      "Current number of predictors before recursive XGBoost = 12 \n",
      "    Taking top 3 features per iteration...\n",
      "    XGBoost version using 1.5.2 as tree method: hist\n",
      "Number of booster rounds = 100\n",
      "        using 12 variables...\n",
      "            Time taken for regular XGBoost feature selection = 0 seconds\n",
      "        using 10 variables...\n",
      "            Time taken for regular XGBoost feature selection = 0 seconds\n",
      "        using 8 variables...\n",
      "            Time taken for regular XGBoost feature selection = 0 seconds\n",
      "        using 6 variables...\n",
      "            Time taken for regular XGBoost feature selection = 0 seconds\n",
      "        using 4 variables...\n",
      "            Time taken for regular XGBoost feature selection = 0 seconds\n",
      "    Completed XGBoost feature selection in 0 seconds\n",
      "#######################################################################################\n",
      "#####          F E A T U R E   S E L E C T I O N   C O M P L E T E D            #######\n",
      "#######################################################################################\n",
      "Selected 6 important features:\n",
      "['LSTAT', 'RM', 'DIS', 'NOX', 'PTRATIO', 'TAX']\n",
      "Total Time taken for featurewiz selection = 2 seconds\n",
      "Output contains two dataframes: train and test with 6 important features.\n"
     ]
    }
   ],
   "source": [
    "trainm, testm = FW.featurewiz(train, target,  test_data=test, verbose=1,# corr_limit=0.90, \n",
    "#        sep=',', header=0,feature_engg='', category_encoders='',\n",
    "#\t\tdask_xgboost_flag=False, nrows=None\n",
    "                             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa4a6e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#### Single_Label Regression problem ####\n",
      "# Simple XGBoost is a good choice compared to complex_XGBoost_model for Regression problems.\n",
      "    Hyper Param Tuning XGBoost with CPU parameters. This will take time. Please be patient...\n",
      "[0]\tvalidation_0-rmse:22.44154\n",
      "[1]\tvalidation_0-rmse:21.76996\n",
      "[2]\tvalidation_0-rmse:21.13335\n",
      "[3]\tvalidation_0-rmse:20.49731\n",
      "[4]\tvalidation_0-rmse:19.90981\n",
      "[5]\tvalidation_0-rmse:19.30797\n",
      "[6]\tvalidation_0-rmse:18.74095\n",
      "[7]\tvalidation_0-rmse:18.19146\n",
      "[8]\tvalidation_0-rmse:17.64797\n",
      "[9]\tvalidation_0-rmse:17.16114\n",
      "[10]\tvalidation_0-rmse:16.62407\n",
      "[11]\tvalidation_0-rmse:16.12639\n",
      "[12]\tvalidation_0-rmse:15.63559\n",
      "[13]\tvalidation_0-rmse:15.15839\n",
      "[14]\tvalidation_0-rmse:14.72383\n",
      "[15]\tvalidation_0-rmse:14.30304\n",
      "[16]\tvalidation_0-rmse:13.89959\n",
      "[17]\tvalidation_0-rmse:13.49292\n",
      "[18]\tvalidation_0-rmse:13.11760\n",
      "[19]\tvalidation_0-rmse:12.75288\n",
      "[20]\tvalidation_0-rmse:12.41028\n",
      "[21]\tvalidation_0-rmse:12.06922\n",
      "[22]\tvalidation_0-rmse:11.72452\n",
      "[23]\tvalidation_0-rmse:11.39889\n",
      "[24]\tvalidation_0-rmse:11.08278\n",
      "[25]\tvalidation_0-rmse:10.78942\n",
      "[26]\tvalidation_0-rmse:10.49035\n",
      "[27]\tvalidation_0-rmse:10.20727\n",
      "[28]\tvalidation_0-rmse:9.93463\n",
      "[29]\tvalidation_0-rmse:9.67776\n",
      "[30]\tvalidation_0-rmse:9.41394\n",
      "[31]\tvalidation_0-rmse:9.16279\n",
      "[32]\tvalidation_0-rmse:8.92936\n",
      "[33]\tvalidation_0-rmse:8.70722\n",
      "[34]\tvalidation_0-rmse:8.47082\n",
      "[35]\tvalidation_0-rmse:8.27217\n",
      "[36]\tvalidation_0-rmse:8.07197\n",
      "[37]\tvalidation_0-rmse:7.85769\n",
      "[38]\tvalidation_0-rmse:7.67669\n",
      "[39]\tvalidation_0-rmse:7.48286\n",
      "[40]\tvalidation_0-rmse:7.28345\n",
      "[41]\tvalidation_0-rmse:7.09488\n",
      "[42]\tvalidation_0-rmse:6.93035\n",
      "[43]\tvalidation_0-rmse:6.74967\n",
      "[44]\tvalidation_0-rmse:6.58881\n",
      "[45]\tvalidation_0-rmse:6.44435\n",
      "[46]\tvalidation_0-rmse:6.28675\n",
      "[47]\tvalidation_0-rmse:6.15451\n",
      "[48]\tvalidation_0-rmse:6.03089\n",
      "[49]\tvalidation_0-rmse:5.90888\n",
      "[50]\tvalidation_0-rmse:5.78976\n",
      "[51]\tvalidation_0-rmse:5.65475\n",
      "[52]\tvalidation_0-rmse:5.53192\n",
      "[53]\tvalidation_0-rmse:5.41939\n",
      "[54]\tvalidation_0-rmse:5.30512\n",
      "[55]\tvalidation_0-rmse:5.21249\n",
      "[56]\tvalidation_0-rmse:5.10605\n",
      "[57]\tvalidation_0-rmse:5.00028\n",
      "[58]\tvalidation_0-rmse:4.89894\n",
      "[59]\tvalidation_0-rmse:4.81359\n",
      "[60]\tvalidation_0-rmse:4.71517\n",
      "[61]\tvalidation_0-rmse:4.62659\n",
      "[62]\tvalidation_0-rmse:4.54210\n",
      "[63]\tvalidation_0-rmse:4.46106\n",
      "[64]\tvalidation_0-rmse:4.39123\n",
      "[65]\tvalidation_0-rmse:4.31438\n",
      "[66]\tvalidation_0-rmse:4.25034\n",
      "[67]\tvalidation_0-rmse:4.19208\n",
      "[68]\tvalidation_0-rmse:4.13733\n",
      "[69]\tvalidation_0-rmse:4.07425\n",
      "[70]\tvalidation_0-rmse:4.02456\n",
      "[71]\tvalidation_0-rmse:3.98085\n",
      "[72]\tvalidation_0-rmse:3.92890\n",
      "[73]\tvalidation_0-rmse:3.88932\n",
      "[74]\tvalidation_0-rmse:3.85035\n",
      "[75]\tvalidation_0-rmse:3.81519\n",
      "[76]\tvalidation_0-rmse:3.77698\n",
      "[77]\tvalidation_0-rmse:3.73584\n",
      "[78]\tvalidation_0-rmse:3.69120\n",
      "[79]\tvalidation_0-rmse:3.65526\n",
      "[80]\tvalidation_0-rmse:3.62191\n",
      "[81]\tvalidation_0-rmse:3.59403\n",
      "[82]\tvalidation_0-rmse:3.56156\n",
      "[83]\tvalidation_0-rmse:3.53182\n",
      "[84]\tvalidation_0-rmse:3.50033\n",
      "[85]\tvalidation_0-rmse:3.46663\n",
      "[86]\tvalidation_0-rmse:3.44848\n",
      "[87]\tvalidation_0-rmse:3.42002\n",
      "[88]\tvalidation_0-rmse:3.39711\n",
      "[89]\tvalidation_0-rmse:3.37055\n",
      "[90]\tvalidation_0-rmse:3.34918\n",
      "[91]\tvalidation_0-rmse:3.33432\n",
      "[92]\tvalidation_0-rmse:3.31150\n",
      "[93]\tvalidation_0-rmse:3.29514\n",
      "[94]\tvalidation_0-rmse:3.27281\n",
      "[95]\tvalidation_0-rmse:3.25799\n",
      "[96]\tvalidation_0-rmse:3.23280\n",
      "[97]\tvalidation_0-rmse:3.21117\n",
      "[98]\tvalidation_0-rmse:3.20203\n",
      "[99]\tvalidation_0-rmse:3.18177\n",
      "[100]\tvalidation_0-rmse:3.15734\n",
      "[101]\tvalidation_0-rmse:3.14638\n",
      "[102]\tvalidation_0-rmse:3.13545\n",
      "[103]\tvalidation_0-rmse:3.11526\n",
      "[104]\tvalidation_0-rmse:3.09775\n",
      "[105]\tvalidation_0-rmse:3.08975\n",
      "[106]\tvalidation_0-rmse:3.08237\n",
      "[107]\tvalidation_0-rmse:3.07377\n",
      "[108]\tvalidation_0-rmse:3.06246\n",
      "[109]\tvalidation_0-rmse:3.05562\n",
      "[110]\tvalidation_0-rmse:3.04409\n",
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      "[112]\tvalidation_0-rmse:3.02882\n",
      "[113]\tvalidation_0-rmse:3.02465\n",
      "[114]\tvalidation_0-rmse:3.01822\n",
      "[115]\tvalidation_0-rmse:3.01042\n",
      "[116]\tvalidation_0-rmse:2.99564\n",
      "[117]\tvalidation_0-rmse:2.98830\n",
      "[118]\tvalidation_0-rmse:2.97944\n",
      "[119]\tvalidation_0-rmse:2.97602\n",
      "[120]\tvalidation_0-rmse:2.97529\n",
      "[121]\tvalidation_0-rmse:2.97293\n",
      "[122]\tvalidation_0-rmse:2.96658\n",
      "[123]\tvalidation_0-rmse:2.96258\n",
      "[124]\tvalidation_0-rmse:2.96392\n",
      "[125]\tvalidation_0-rmse:2.95614\n",
      "[126]\tvalidation_0-rmse:2.95522\n",
      "[127]\tvalidation_0-rmse:2.94862\n",
      "[128]\tvalidation_0-rmse:2.94604\n",
      "[129]\tvalidation_0-rmse:2.93852\n",
      "[130]\tvalidation_0-rmse:2.93780\n",
      "[131]\tvalidation_0-rmse:2.92808\n",
      "[132]\tvalidation_0-rmse:2.91869\n",
      "[133]\tvalidation_0-rmse:2.92229\n",
      "[134]\tvalidation_0-rmse:2.91881\n",
      "[135]\tvalidation_0-rmse:2.91562\n",
      "[136]\tvalidation_0-rmse:2.90911\n",
      "[137]\tvalidation_0-rmse:2.90871\n",
      "[138]\tvalidation_0-rmse:2.90737\n",
      "[139]\tvalidation_0-rmse:2.89382\n",
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      "[141]\tvalidation_0-rmse:2.89291\n",
      "[142]\tvalidation_0-rmse:2.88853\n",
      "[143]\tvalidation_0-rmse:2.88992\n",
      "[144]\tvalidation_0-rmse:2.88584\n",
      "[145]\tvalidation_0-rmse:2.88180\n",
      "[146]\tvalidation_0-rmse:2.87876\n",
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      "[148]\tvalidation_0-rmse:2.86979\n",
      "[149]\tvalidation_0-rmse:2.86390\n",
      "[150]\tvalidation_0-rmse:2.86572\n",
      "[151]\tvalidation_0-rmse:2.86309\n",
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      "[154]\tvalidation_0-rmse:2.85807\n",
      "[155]\tvalidation_0-rmse:2.85339\n",
      "[156]\tvalidation_0-rmse:2.85167\n",
      "[157]\tvalidation_0-rmse:2.84788\n",
      "[158]\tvalidation_0-rmse:2.84463\n",
      "[159]\tvalidation_0-rmse:2.84456\n",
      "[160]\tvalidation_0-rmse:2.84444\n",
      "[161]\tvalidation_0-rmse:2.84459\n",
      "[162]\tvalidation_0-rmse:2.84500\n",
      "[163]\tvalidation_0-rmse:2.84112\n",
      "[164]\tvalidation_0-rmse:2.84294\n",
      "[165]\tvalidation_0-rmse:2.84201\n",
      "[166]\tvalidation_0-rmse:2.84165\n",
      "[167]\tvalidation_0-rmse:2.84081\n",
      "[168]\tvalidation_0-rmse:2.83973\n",
      "[169]\tvalidation_0-rmse:2.83905\n",
      "[170]\tvalidation_0-rmse:2.83936\n",
      "[171]\tvalidation_0-rmse:2.83740\n",
      "[172]\tvalidation_0-rmse:2.83684\n",
      "[173]\tvalidation_0-rmse:2.84010\n",
      "[174]\tvalidation_0-rmse:2.83901\n",
      "[175]\tvalidation_0-rmse:2.83710\n",
      "[176]\tvalidation_0-rmse:2.83710\n",
      "[177]\tvalidation_0-rmse:2.83375\n",
      "[178]\tvalidation_0-rmse:2.83348\n",
      "[179]\tvalidation_0-rmse:2.83253\n",
      "[180]\tvalidation_0-rmse:2.82926\n",
      "[181]\tvalidation_0-rmse:2.82950\n",
      "[182]\tvalidation_0-rmse:2.82772\n",
      "[183]\tvalidation_0-rmse:2.82678\n",
      "[184]\tvalidation_0-rmse:2.82201\n",
      "[185]\tvalidation_0-rmse:2.82051\n",
      "[186]\tvalidation_0-rmse:2.82071\n",
      "[187]\tvalidation_0-rmse:2.81765\n",
      "[188]\tvalidation_0-rmse:2.81711\n",
      "[189]\tvalidation_0-rmse:2.81428\n",
      "[190]\tvalidation_0-rmse:2.81466\n",
      "[191]\tvalidation_0-rmse:2.81715\n",
      "[192]\tvalidation_0-rmse:2.81656\n",
      "[193]\tvalidation_0-rmse:2.81472\n",
      "Time taken for Hyper Param tuning of XGB (in minutes) = 0.2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a5a28be2d9b41d2a98073d49e65e767",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE score in fold 1 = 2.977173697116477\n",
      "RMSE score in fold 2 = 3.611263064975329\n",
      "RMSE score in fold 3 = 3.2843705435332997\n",
      "RMSE score in fold 4 = 3.383184732837887\n",
      "RMSE score in fold 5 = 3.006418342738492\n",
      "    Time taken for Cross Validation of XGBoost (in minutes) = 0.1\n",
      "\n",
      "Cross-validated Average scores are:  3.252482076240297\n",
      "Training model on full train dataset...\n",
      "    Time taken for training XGBoost (in minutes) = 0.0\n",
      "Returning the following:\n",
      "    final predictions [30.41788  28.410368 19.549873 20.595715 19.591265 20.225937 25.007622\n",
      " 18.65737  21.864565 23.829147]\n",
      "    Model = XGBRegressor(alpha=0.015, base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "             colsample_bynode=1, colsample_bytree=0.5, enable_categorical=False,\n",
      "             eval_metric='rmse', gamma=8, gpu_id=0, importance_type=None,\n",
      "             interaction_constraints='', learning_rate=0.031446387626298145,\n",
      "             max_delta_step=0, max_depth=8, min_child_weight=2, missing=nan,\n",
      "             monotone_constraints='()', n_estimators=485, n_jobs=-1,\n",
      "             num_parallel_tree=1, predictor='cpu_predictor', random_state=99,\n",
      "             reg_alpha=0, reg_lambda=0.5, scale_pos_weight=1, silent=True,\n",
      "             subsample=0.7, tree_method='hist', updater='grow_colmaker', ...)\n"
     ]
    }
   ],
   "source": [
    "from featurewiz import simple_XGBoost_model\n",
    "y_preds = simple_XGBoost_model(train[preds], train[target], test[preds])\n",
    "ypreds1 = y_preds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b7348790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats = testm.columns.tolist()\n",
    "len(feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d78aaf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#### Single_Label Regression problem ####\n",
      "# Simple XGBoost is a good choice compared to complex_XGBoost_model for Regression problems.\n",
      "    Hyper Param Tuning XGBoost with CPU parameters. This will take time. Please be patient...\n",
      "[0]\tvalidation_0-rmse:22.38506\n",
      "[1]\tvalidation_0-rmse:21.74566\n",
      "[2]\tvalidation_0-rmse:21.11743\n",
      "[3]\tvalidation_0-rmse:20.50002\n",
      "[4]\tvalidation_0-rmse:19.88286\n",
      "[5]\tvalidation_0-rmse:19.29656\n",
      "[6]\tvalidation_0-rmse:18.74860\n",
      "[7]\tvalidation_0-rmse:18.20086\n",
      "[8]\tvalidation_0-rmse:17.66310\n",
      "[9]\tvalidation_0-rmse:17.13972\n",
      "[10]\tvalidation_0-rmse:16.67624\n",
      "[11]\tvalidation_0-rmse:16.17764\n",
      "[12]\tvalidation_0-rmse:15.70590\n",
      "[13]\tvalidation_0-rmse:15.24260\n",
      "[14]\tvalidation_0-rmse:14.79668\n",
      "[15]\tvalidation_0-rmse:14.36594\n",
      "[16]\tvalidation_0-rmse:13.96215\n",
      "[17]\tvalidation_0-rmse:13.55664\n",
      "[18]\tvalidation_0-rmse:13.17717\n",
      "[19]\tvalidation_0-rmse:12.81302\n",
      "[20]\tvalidation_0-rmse:12.44554\n",
      "[21]\tvalidation_0-rmse:12.10055\n",
      "[22]\tvalidation_0-rmse:11.76230\n",
      "[23]\tvalidation_0-rmse:11.42632\n",
      "[24]\tvalidation_0-rmse:11.09725\n",
      "[25]\tvalidation_0-rmse:10.78459\n",
      "[26]\tvalidation_0-rmse:10.48684\n",
      "[27]\tvalidation_0-rmse:10.20928\n",
      "[28]\tvalidation_0-rmse:9.94685\n",
      "[29]\tvalidation_0-rmse:9.69379\n",
      "[30]\tvalidation_0-rmse:9.45852\n",
      "[31]\tvalidation_0-rmse:9.22297\n",
      "[32]\tvalidation_0-rmse:8.98810\n",
      "[33]\tvalidation_0-rmse:8.74991\n",
      "[34]\tvalidation_0-rmse:8.52826\n",
      "[35]\tvalidation_0-rmse:8.30350\n",
      "[36]\tvalidation_0-rmse:8.11091\n",
      "[37]\tvalidation_0-rmse:7.91933\n",
      "[38]\tvalidation_0-rmse:7.71944\n",
      "[39]\tvalidation_0-rmse:7.52850\n",
      "[40]\tvalidation_0-rmse:7.35433\n",
      "[41]\tvalidation_0-rmse:7.16370\n",
      "[42]\tvalidation_0-rmse:6.98240\n",
      "[43]\tvalidation_0-rmse:6.81198\n",
      "[44]\tvalidation_0-rmse:6.66207\n",
      "[45]\tvalidation_0-rmse:6.51441\n",
      "[46]\tvalidation_0-rmse:6.36804\n",
      "[47]\tvalidation_0-rmse:6.24166\n",
      "[48]\tvalidation_0-rmse:6.12019\n",
      "[49]\tvalidation_0-rmse:6.00523\n",
      "[50]\tvalidation_0-rmse:5.88655\n",
      "[51]\tvalidation_0-rmse:5.75879\n",
      "[52]\tvalidation_0-rmse:5.62858\n",
      "[53]\tvalidation_0-rmse:5.50899\n",
      "[54]\tvalidation_0-rmse:5.40443\n",
      "[55]\tvalidation_0-rmse:5.30399\n",
      "[56]\tvalidation_0-rmse:5.21360\n",
      "[57]\tvalidation_0-rmse:5.13146\n",
      "[58]\tvalidation_0-rmse:5.05088\n",
      "[59]\tvalidation_0-rmse:4.95176\n",
      "[60]\tvalidation_0-rmse:4.86838\n",
      "[61]\tvalidation_0-rmse:4.78782\n",
      "[62]\tvalidation_0-rmse:4.71493\n",
      "[63]\tvalidation_0-rmse:4.63872\n",
      "[64]\tvalidation_0-rmse:4.57764\n",
      "[65]\tvalidation_0-rmse:4.50907\n",
      "[66]\tvalidation_0-rmse:4.44235\n",
      "[67]\tvalidation_0-rmse:4.36493\n",
      "[68]\tvalidation_0-rmse:4.30427\n",
      "[69]\tvalidation_0-rmse:4.25056\n",
      "[70]\tvalidation_0-rmse:4.18977\n",
      "[71]\tvalidation_0-rmse:4.13744\n",
      "[72]\tvalidation_0-rmse:4.09251\n",
      "[73]\tvalidation_0-rmse:4.04458\n",
      "[74]\tvalidation_0-rmse:4.00799\n",
      "[75]\tvalidation_0-rmse:3.97592\n",
      "[76]\tvalidation_0-rmse:3.93571\n",
      "[77]\tvalidation_0-rmse:3.88373\n",
      "[78]\tvalidation_0-rmse:3.85051\n",
      "[79]\tvalidation_0-rmse:3.82105\n",
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      "[82]\tvalidation_0-rmse:3.70982\n",
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      "[84]\tvalidation_0-rmse:3.66191\n",
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      "[87]\tvalidation_0-rmse:3.57506\n",
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      "[91]\tvalidation_0-rmse:3.48813\n",
      "[92]\tvalidation_0-rmse:3.47284\n",
      "[93]\tvalidation_0-rmse:3.44528\n",
      "[94]\tvalidation_0-rmse:3.42790\n",
      "[95]\tvalidation_0-rmse:3.41111\n",
      "[96]\tvalidation_0-rmse:3.39971\n",
      "[97]\tvalidation_0-rmse:3.38431\n",
      "[98]\tvalidation_0-rmse:3.36653\n",
      "[99]\tvalidation_0-rmse:3.35580\n",
      "[100]\tvalidation_0-rmse:3.34329\n",
      "[101]\tvalidation_0-rmse:3.33525\n",
      "[102]\tvalidation_0-rmse:3.32761\n",
      "[103]\tvalidation_0-rmse:3.30621\n",
      "[104]\tvalidation_0-rmse:3.29153\n",
      "[105]\tvalidation_0-rmse:3.27303\n",
      "[106]\tvalidation_0-rmse:3.26189\n",
      "[107]\tvalidation_0-rmse:3.25414\n",
      "[108]\tvalidation_0-rmse:3.25155\n",
      "[109]\tvalidation_0-rmse:3.23789\n",
      "[110]\tvalidation_0-rmse:3.23009\n",
      "[111]\tvalidation_0-rmse:3.22342\n",
      "[112]\tvalidation_0-rmse:3.21070\n",
      "[113]\tvalidation_0-rmse:3.20209\n",
      "[114]\tvalidation_0-rmse:3.19664\n",
      "[115]\tvalidation_0-rmse:3.18942\n",
      "[116]\tvalidation_0-rmse:3.18527\n",
      "[117]\tvalidation_0-rmse:3.18452\n",
      "[118]\tvalidation_0-rmse:3.17748\n",
      "[119]\tvalidation_0-rmse:3.17583\n",
      "[120]\tvalidation_0-rmse:3.17117\n",
      "[121]\tvalidation_0-rmse:3.16453\n",
      "[122]\tvalidation_0-rmse:3.16467\n",
      "[123]\tvalidation_0-rmse:3.16395\n",
      "[124]\tvalidation_0-rmse:3.16342\n",
      "[125]\tvalidation_0-rmse:3.16152\n",
      "[126]\tvalidation_0-rmse:3.15478\n",
      "[127]\tvalidation_0-rmse:3.14938\n",
      "[128]\tvalidation_0-rmse:3.14777\n",
      "[129]\tvalidation_0-rmse:3.14143\n",
      "[130]\tvalidation_0-rmse:3.13170\n",
      "[131]\tvalidation_0-rmse:3.12702\n",
      "[132]\tvalidation_0-rmse:3.12537\n",
      "[133]\tvalidation_0-rmse:3.12611\n",
      "[134]\tvalidation_0-rmse:3.11913\n",
      "[135]\tvalidation_0-rmse:3.11554\n",
      "[136]\tvalidation_0-rmse:3.11441\n",
      "[137]\tvalidation_0-rmse:3.10883\n",
      "[138]\tvalidation_0-rmse:3.10850\n",
      "[139]\tvalidation_0-rmse:3.10601\n",
      "[140]\tvalidation_0-rmse:3.10600\n",
      "[141]\tvalidation_0-rmse:3.09988\n",
      "[142]\tvalidation_0-rmse:3.09689\n",
      "[143]\tvalidation_0-rmse:3.10160\n",
      "[144]\tvalidation_0-rmse:3.10179\n",
      "[145]\tvalidation_0-rmse:3.10244\n",
      "[146]\tvalidation_0-rmse:3.10245\n",
      "[147]\tvalidation_0-rmse:3.09466\n",
      "[148]\tvalidation_0-rmse:3.09215\n",
      "[149]\tvalidation_0-rmse:3.09104\n",
      "[150]\tvalidation_0-rmse:3.08783\n",
      "[151]\tvalidation_0-rmse:3.08801\n",
      "[152]\tvalidation_0-rmse:3.08777\n",
      "[153]\tvalidation_0-rmse:3.09151\n",
      "[154]\tvalidation_0-rmse:3.09277\n",
      "[155]\tvalidation_0-rmse:3.09146\n",
      "[156]\tvalidation_0-rmse:3.09157\n",
      "[157]\tvalidation_0-rmse:3.08919\n",
      "Time taken for Hyper Param tuning of XGB (in minutes) = 0.1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c62b77771b8c40e7a865f5d1ba449e30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE score in fold 1 = 3.3060150945700544\n",
      "RMSE score in fold 2 = 3.559584607283754\n",
      "RMSE score in fold 3 = 3.783236984277747\n",
      "RMSE score in fold 4 = 3.6037810232239154\n",
      "RMSE score in fold 5 = 3.335399673070716\n",
      "    Time taken for Cross Validation of XGBoost (in minutes) = 0.1\n",
      "\n",
      "Cross-validated Average scores are:  3.5176034764852373\n",
      "Training model on full train dataset...\n",
      "    Time taken for training XGBoost (in minutes) = 0.0\n",
      "Returning the following:\n",
      "    final predictions [31.001469 28.173975 20.603096 21.598976 18.233286 19.724012 25.600805\n",
      " 18.874134 21.594769 22.505758]\n",
      "    Model = XGBRegressor(alpha=0.015, base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "             colsample_bynode=1, colsample_bytree=0.5, enable_categorical=False,\n",
      "             eval_metric='rmse', gamma=8, gpu_id=0, importance_type=None,\n",
      "             interaction_constraints='', learning_rate=0.031446387626298145,\n",
      "             max_delta_step=0, max_depth=8, min_child_weight=2, missing=nan,\n",
      "             monotone_constraints='()', n_estimators=485, n_jobs=-1,\n",
      "             num_parallel_tree=1, predictor='cpu_predictor', random_state=99,\n",
      "             reg_alpha=0, reg_lambda=0.5, scale_pos_weight=1, silent=True,\n",
      "             subsample=0.7, tree_method='hist', updater='grow_colmaker', ...)\n"
     ]
    }
   ],
   "source": [
    "y_preds = simple_XGBoost_model(trainm[feats], trainm[target], testm[feats])\n",
    "ypreds2 = y_preds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2b96359a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported LazyTransformer version:1.00. Call by using:\n",
      "    lazy = LazyTransformer(model=None, encoders='auto', scalers=None, date_to_string=False,\n",
      "        transform_target=False, imbalanced=False, save=False, combine_rare=False, verbose=0)\n",
      "    ### if you are not using a model in pipeline, you must use fit and transform ###\n",
      "        X_trainm, y_trainm = lazy.fit_transform(X_train, y_train)\n",
      "        X_testm = lazy.transform(X_test)\n",
      "    ### If using a model in pipeline, use fit and predict only ###\n",
      "        lazy.fit(X_train, y_train)\n",
      "        lazy.predict(X_test)\n",
      "\n",
      "    R-Squared = 91%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2.9051367221244933"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lazytransform import print_regression_model_stats, print_sulo_accuracy\n",
    "print_regression_model_stats(test[target], ypreds1)\n",
    "#print_sulo_accuracy(test[target], ypreds1,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e3430b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    R-Squared = 92%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2.795406291173381"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lazytransform import print_regression_model_stats\n",
    "print_regression_model_stats(test[target], ypreds2)\n",
    "#print_sulo_accuracy(test[target], ypreds2,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30cc2d30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66baac45",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
